This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of Training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the Training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
pop3代理服務器源代碼One of the most powerful features of Pop3 Agent is a naive Bayes filter,
that is capable of recognizing spam e-mails after appropriate Training.
Pop3 Agent uses an embedded Firebird database server. Of course, you can
configure Pop3 Agent to work with an existing server if there is another
Interbase/Firebird installation available in your network. Open the Pop3 Agent
home directory, delete or rename gds32.dll and ib_util.dll, and set the
INI file parameter FBembedded=0.
This code in this directory implements the binary hopfield network.
Source code may be found in HOPNET.CPP. A sample Training file is
H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST,
H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the
screen and a file, ARCHIVE.LST.
palm編成,這種書很少,有興趣看看
Title: Palm Programming: The Developer s Guide
URL: http://safari.oreilly.com/JVXSL.asp?x=1&mode=section&sortKey=rank&sortOrder=desc&view=book&xmlid=1-56592-525-4&open=false&srchText=palm+programming&code=&h=&m=&l=1&catid=&s=1&b=1&f=1&t=1&c=1&u=1&page=0
ISBN: 1-56592-525-4
Author: Julie McKeehan/ Neil Rhodes
Publisher: O Reilly
Page: 478
Edition: 1st edition (December 1998)
Catalog: PDA programming / Palm
Format: pdf
Size: 2.06M
Supplier:
Summary: Emerging as the bestselling hand-held computers of all time, PalmPilots have spawned intense developer activity and a fanatical following. Used by Palm in their developer Training, this tutorial-style book shows intermediate to experienced C programmers how to build a Palm application from the ground up. Includes a CD-ROM with source code and third-party developer tools
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of Training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) Training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing Training from millions of examples by hundreds of features in a reasonable time/memory.
